{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第9章 一元线性回归"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 本章需要用到的库\n",
    "import numpy as np # 导入numpy库\n",
    "import pandas as pd # 导入pandas库\n",
    "import matplotlib.pyplot as plt # 导入matplotlib库\n",
    "import seaborn as sns # 导入seaborn库\n",
    "from scipy.stats import pearsonr # 导入Pearson相关系数函数\n",
    "from scipy.stats import f # 导入F分布函数\n",
    "import statsmodels.api as sm # 导入statsmodels库\n",
    "from statsmodels.formula.api import ols # 导入普通最小二乘法函数\n",
    "from statsmodels.stats.anova import anova_lm # 导入方差分析函数\n",
    "from statsmodels.stats.outliers_influence import summary_table # 导入影响汇总表函数\n",
    "from statsmodels.stats.stattools import durbin_watson # 导入DW检验函数\n",
    "\n",
    "# 设置初始化\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.1 确定变量间的关系"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1.1 变量间的关系"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1.2 相关关系的描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123) # 设置随机种子\n",
    "plt.subplots(3, 2, figsize=(8, 10)) # 设置画布大小(12, 12\n",
    "\n",
    "plt.subplot(321) # 绘制第一个子图\n",
    "x1 = np.random.uniform(0, 25, size=50) # 生成自变量x1\n",
    "y1 = x1 + 5 + np.random.normal(0, 2, size=50) # 生成因变量y1\n",
    "sns.regplot(x=x1, y=y1, marker='+') # 绘制散点图和回归线\n",
    "plt.xlim(0, 25) # 设置x轴的范围\n",
    "plt.xlabel('x1') # 设置x轴标签\n",
    "plt.ylabel('y1') # 设置y轴标签\n",
    "plt.title('(a) 正线性相关') # 设置子图标题\n",
    "\n",
    "plt.subplot(322) # 绘制第二个子图\n",
    "x2 = np.random.uniform(0, 25, size=50) # 生成自变量x2\n",
    "y2 = - x2 - 5 + np.random.normal(0, 2, size=50) # 生成因变量y2\n",
    "sns.regplot(x=x2, y=y2, marker='+') # 绘制散点图和回归线\n",
    "plt.xlim(0, 25) # 设置x轴的范围\n",
    "plt.xlabel('x2') # 设置x轴标签\n",
    "plt.ylabel('y2') # 设置y轴标签\n",
    "plt.title('(b) 负线性相关') # 设置子图标题\n",
    "\n",
    "plt.subplot(323) # 绘制第三个子图\n",
    "x3 = np.linspace(0, 25, 25) # 生成自变量x3\n",
    "y3 = x3 + 5 # 生成因变量y3\n",
    "sns.regplot(x=x3, y=y3, marker='o') # 绘制散点图和回归线\n",
    "plt.xlim(0, 25) # 设置x轴的范围\n",
    "plt.xlabel('x3') # 设置x轴标签\n",
    "plt.ylabel('y3') # 设置y轴标签\n",
    "plt.title('(c) 完全正线性相关') # 设置子图标题\n",
    "\n",
    "plt.subplot(324) # 绘制第四个子图\n",
    "x4 = np.linspace(0, 25, 25) # 生成自变量x4\n",
    "y4 = - x4 + 25 # 生成因变量y4\n",
    "sns.regplot(x=x4, y=y4, marker='o') # 绘制散点图和回归线\n",
    "plt.xlim(0, 25) # 设置x轴的范围\n",
    "plt.xlabel('x4') # 设置x轴标签\n",
    "plt.ylabel('y4') # 设置y轴标签\n",
    "plt.title('(d) 完全负线性相关') # 设置子图标题\n",
    "\n",
    "plt.subplot(325) # 绘制第五个子图\n",
    "x5 = np.random.uniform(0, 25, size=50) # 生成自变量x5\n",
    "y5 = -x5**2 + 30*x5 + 50 + np.random.normal(0, 10, size=50) # 生成因变量y5\n",
    "sns.scatterplot(x=x5, y=y5, marker='o') # 绘制散点图\n",
    "sns.lineplot(x=x5, y=-x5**2 + 30*x5 + 50, color='steelblue') # 绘制方程图像\n",
    "plt.xlim(0, 25) # 设置x轴的范围\n",
    "plt.xlabel('x5') # 设置x轴标签\n",
    "plt.ylabel('y5') # 设置y轴标签\n",
    "plt.title('(e) 非线性相关') # 设置子图标题\n",
    "\n",
    "plt.subplot(326) # 绘制第六个子图\n",
    "x6 = np.random.uniform(0, 25, size=50) # 生成自变量x6\n",
    "y6 = np.random.normal(15, 2, size=50) # 生成因变量y6\n",
    "sns.regplot(x=x6, y=y6, marker='o') # 绘制散点图和回归线\n",
    "plt.xlim(0, 25) # 设置x轴的范围\n",
    "plt.xlabel('x6') # 设置x轴标签\n",
    "plt.ylabel('y6') # 设置y轴标签\n",
    "plt.title('(f) 不相关') # 设置子图标题\n",
    "\n",
    "plt.tight_layout() # 设置子图的间距\n",
    "plt.show() # 展示图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企业编号</th>\n",
       "      <th>销售收入</th>\n",
       "      <th>广告支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4597.5</td>\n",
       "      <td>338.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6611.0</td>\n",
       "      <td>811.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7349.3</td>\n",
       "      <td>723.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5525.7</td>\n",
       "      <td>514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4675.9</td>\n",
       "      <td>426.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   企业编号    销售收入   广告支出\n",
       "0     1  4597.5  338.6\n",
       "1     2  6611.0  811.0\n",
       "2     3  7349.3  723.5\n",
       "3     4  5525.7  514.0\n",
       "4     5  4675.9  426.4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example9_1 = pd.read_csv('./pydata/chap09/example9_1.csv', encoding='gbk') # 读取数据\n",
    "example9_1.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制销售收入与广告支出的散点图\n",
    "sns.jointplot(\n",
    "    x='广告支出', y='销售收入', data=example9_1, \n",
    "    kind='reg', truncate=False, color='steelblue',\n",
    "    height=6, ratio=3, marginal_ticks=True\n",
    ") # 绘制散点图和回归线\n",
    "plt.show() # 展示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1.3 关系强度的度量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出假设：\n",
    "- $H_0: \\rho = 0$(两个变量的线性关系不显著)\n",
    "- $H_1: \\rho \\ne 0$(两个变量的线性关系显著)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二者的相关系数为0.9371, 检验的p值为1.161e-09\n"
     ]
    }
   ],
   "source": [
    "corr, p_value = pearsonr(example9_1['广告支出'], example9_1['销售收入']) # 计算相关系数和p值\n",
    "print(f'二者的相关系数为{corr:.4f}, 检验的p值为{p_value:.4g}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05, 所以拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.2 模型的估计和检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.2.1 回归模型与回归方程"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.2.2 参数的最小二乘估计"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "估计方程：$\\hat{y} = b_0 + b_1 \\times 广告支出$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>销售收入</td>       <th>  R-squared:         </th> <td>   0.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   129.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th> <td>1.16e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:43:20</td>     <th>  Log-Likelihood:    </th> <td> -146.85</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>   297.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    18</td>      <th>  BIC:               </th> <td>   299.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 2343.8916</td> <td>  274.483</td> <td>    8.539</td> <td> 0.000</td> <td> 1767.225</td> <td> 2920.558</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>广告支出</th>      <td>    5.6735</td> <td>    0.498</td> <td>   11.391</td> <td> 0.000</td> <td>    4.627</td> <td>    6.720</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.082</td> <th>  Durbin-Watson:     </th> <td>   1.679</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.353</td> <th>  Jarque-Bera (JB):  </th> <td>   0.854</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.473</td> <th>  Prob(JB):          </th> <td>   0.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.360</td> <th>  Cond. No.          </th> <td>1.72e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.72e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   销售收入   R-squared:                       0.878\n",
       "Model:                            OLS   Adj. R-squared:                  0.871\n",
       "Method:                 Least Squares   F-statistic:                     129.8\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):           1.16e-09\n",
       "Time:                        20:43:20   Log-Likelihood:                -146.85\n",
       "No. Observations:                  20   AIC:                             297.7\n",
       "Df Residuals:                      18   BIC:                             299.7\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   2343.8916    274.483      8.539      0.000    1767.225    2920.558\n",
       "广告支出           5.6735      0.498     11.391      0.000       4.627       6.720\n",
       "==============================================================================\n",
       "Omnibus:                        2.082   Durbin-Watson:                   1.679\n",
       "Prob(Omnibus):                  0.353   Jarque-Bera (JB):                0.854\n",
       "Skew:                           0.473   Prob(JB):                        0.652\n",
       "Kurtosis:                       3.360   Cond. No.                     1.72e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.72e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols('销售收入 ~ 广告支出', data=example9_1).fit() # 拟合普通最小二乘回归模型\n",
    "model.summary() # 显示回归结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>广告支出</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.013930e+07</td>\n",
       "      <td>2.013930e+07</td>\n",
       "      <td>129.762217</td>\n",
       "      <td>1.161175e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.793629e+06</td>\n",
       "      <td>1.552016e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df        sum_sq       mean_sq           F        PR(>F)\n",
       "广告支出       1.0  2.013930e+07  2.013930e+07  129.762217  1.161175e-09\n",
       "Residual  18.0  2.793629e+06  1.552016e+05         NaN           NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_table = anova_lm(model, typ=1) # 显示方差分析表\n",
    "anova_table"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "拟合结果：$\\hat{y} = 2343.8916 + 5.6735 \\times 广告支出$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制拟合图\n",
    "fig, ax = plt.subplots(figsize=(9, 6)) # 设置画布大小\n",
    "sm.graphics.plot_fit(model, exog_idx='广告支出', ax=ax) # 绘制拟合图\n",
    "plt.plot(example9_1['广告支出'], model.fittedvalues, 'r') # 绘制拟合线\n",
    "plt.annotate(\n",
    "    text=f'$\\hat y={model.params[0]:.2f}+{model.params[1]:.2f} \\\\times$ 广告支出', # 设置注释文本\n",
    "    xy=(550, 5000), xytext=(600, 4000), \n",
    "    arrowprops={'headwidth': 10, 'headlength': 5, 'width': 4, 'facecolor': 'r', 'shrink': 0.1}, \n",
    "    fontsize=14, color='red', ha='left'\n",
    ") # 绘制注释\n",
    "plt.show() # 展示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.2.3 模型的拟合优度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 误差分解示意图\n",
    "plt.figure(figsize=(9, 6)) # 设置画布大小\n",
    "plt.plot(example9_1['广告支出'], example9_1['销售收入'], 'o') # 绘制散点图\n",
    "plt.plot(example9_1['广告支出'], model.fittedvalues, 'steelblue') # 绘制拟合线\n",
    "plt.axhline(y=example9_1['销售收入'].mean(), color='steelblue', linestyle='--') # 绘制均值线\n",
    "for i in range(example9_1.shape[0]): # 遍历每个观测点\n",
    "    plt.plot(\n",
    "        [example9_1['广告支出'][i], example9_1['广告支出'][i]],\n",
    "        [example9_1['销售收入'][i], model.fittedvalues[i]],\n",
    "        'k--', linewidth=1\n",
    "    ) # 绘制误差线\n",
    "plt.show() # 展示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$S_e = \\sqrt{\\frac{\\sum (y_i - \\hat{y}_i)^2}{n-k-1}} = \\sqrt{\\frac{SSE}{n-k-1}} = \\sqrt{MSE}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "残差标准误为393.96\n"
     ]
    }
   ],
   "source": [
    "# 计算残差标准误-根据方差分析表\n",
    "rse = np.sqrt(anova_table.loc['Residual', 'mean_sq'])\n",
    "print(f'残差标准误为{rse:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "残差标准误为393.96\n"
     ]
    }
   ],
   "source": [
    "# 计算残差标准误-根据回归模型\n",
    "print(f'残差标准误为{model.mse_resid**0.5:.2f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.2.4 模型的显著性检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$F = \\frac{SSR/k}{SSE/(n-k-1)} = \\frac{MSR}{MSE} \\sim F(k, n-k-1)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F值为129.76, p值为1.161e-09\n"
     ]
    }
   ],
   "source": [
    "# F检验-根据方差分析表\n",
    "F = anova_table.loc['广告支出', 'mean_sq'] / anova_table.loc['Residual', 'mean_sq']\n",
    "p_value = 1 - f.cdf(F, dfn=anova_table.loc['广告支出', 'df'], dfd=anova_table.loc['Residual', 'df'])\n",
    "print(f'F值为{F:.2f}, p值为{p_value:.4g}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F值为129.76, p值为1.161e-09\n"
     ]
    }
   ],
   "source": [
    "# F检验-根据回归模型\n",
    "print(f'F值为{model.fvalue:.2f}, p值为{model.f_pvalue:.4g}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "广告支出回归系数的t检验\n",
      "t值为11.39, p值为1.161e-09\n"
     ]
    }
   ],
   "source": [
    "# t检验\n",
    "print(r'广告支出回归系数的t检验')\n",
    "print(f't值为{model.tvalues[1]:.2f}, p值为{model.pvalues[1]:.4g}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.3 利用回归方程进行预测"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.3.1 均值的置信区间"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\hat {y}_0 \\pm t_{\\alpha/2} s_{\\hat{y}_0} = \\hat {y}_0 \\pm t_{\\alpha/2} s_e \\sqrt{\\frac{1}{n} + \\frac{(x_0 - \\bar{x})^2}{\\sum (x_i - \\bar{x})^2}}$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.3.2 个别值的预测区间"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\hat {y}_0 \\pm t_{\\alpha/2} s_{ind} = \\hat {y}_0 \\pm t_{\\alpha/2} s_e \\sqrt{1 + \\frac{1}{n} + \\frac{(x_0 - \\bar{x})^2}{\\sum (x_i - \\bar{x})^2}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Obs</th>\n",
       "      <th>Dep Var Population</th>\n",
       "      <th>Predicted Value</th>\n",
       "      <th>Std Error Mean Predict</th>\n",
       "      <th>Mean ci 95% low</th>\n",
       "      <th>Mean ci 95% upp</th>\n",
       "      <th>Predict ci 95% low</th>\n",
       "      <th>Predict ci 95% upp</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Std Error Residual</th>\n",
       "      <th>Student Residual</th>\n",
       "      <th>Cook's D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4597.5</td>\n",
       "      <td>4264.92</td>\n",
       "      <td>126.89</td>\n",
       "      <td>3998.35</td>\n",
       "      <td>4531.50</td>\n",
       "      <td>3395.38</td>\n",
       "      <td>5134.47</td>\n",
       "      <td>332.58</td>\n",
       "      <td>372.96</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6611.0</td>\n",
       "      <td>6945.07</td>\n",
       "      <td>168.77</td>\n",
       "      <td>6590.49</td>\n",
       "      <td>7299.64</td>\n",
       "      <td>6044.64</td>\n",
       "      <td>7845.49</td>\n",
       "      <td>-334.07</td>\n",
       "      <td>355.97</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7349.3</td>\n",
       "      <td>6448.64</td>\n",
       "      <td>133.55</td>\n",
       "      <td>6168.06</td>\n",
       "      <td>6729.22</td>\n",
       "      <td>5574.70</td>\n",
       "      <td>7322.58</td>\n",
       "      <td>900.66</td>\n",
       "      <td>370.63</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5525.7</td>\n",
       "      <td>5260.05</td>\n",
       "      <td>88.18</td>\n",
       "      <td>5074.79</td>\n",
       "      <td>5445.31</td>\n",
       "      <td>4411.90</td>\n",
       "      <td>6108.20</td>\n",
       "      <td>265.65</td>\n",
       "      <td>383.96</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4675.9</td>\n",
       "      <td>4763.05</td>\n",
       "      <td>100.13</td>\n",
       "      <td>4552.70</td>\n",
       "      <td>4973.41</td>\n",
       "      <td>3909.07</td>\n",
       "      <td>5617.04</td>\n",
       "      <td>-87.15</td>\n",
       "      <td>381.02</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4418.6</td>\n",
       "      <td>4762.49</td>\n",
       "      <td>100.15</td>\n",
       "      <td>4552.08</td>\n",
       "      <td>4972.89</td>\n",
       "      <td>3908.49</td>\n",
       "      <td>5616.48</td>\n",
       "      <td>-343.89</td>\n",
       "      <td>381.01</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5845.4</td>\n",
       "      <td>6196.17</td>\n",
       "      <td>117.80</td>\n",
       "      <td>5948.68</td>\n",
       "      <td>6443.66</td>\n",
       "      <td>5332.29</td>\n",
       "      <td>7060.05</td>\n",
       "      <td>-350.77</td>\n",
       "      <td>375.93</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7313.0</td>\n",
       "      <td>7151.01</td>\n",
       "      <td>184.43</td>\n",
       "      <td>6763.53</td>\n",
       "      <td>7538.49</td>\n",
       "      <td>6237.13</td>\n",
       "      <td>8064.90</td>\n",
       "      <td>161.99</td>\n",
       "      <td>348.12</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5035.4</td>\n",
       "      <td>5015.52</td>\n",
       "      <td>91.69</td>\n",
       "      <td>4822.89</td>\n",
       "      <td>5208.15</td>\n",
       "      <td>4165.73</td>\n",
       "      <td>5865.32</td>\n",
       "      <td>19.88</td>\n",
       "      <td>383.14</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>4322.6</td>\n",
       "      <td>4578.10</td>\n",
       "      <td>108.79</td>\n",
       "      <td>4349.55</td>\n",
       "      <td>4806.65</td>\n",
       "      <td>3719.45</td>\n",
       "      <td>5436.75</td>\n",
       "      <td>-255.50</td>\n",
       "      <td>378.64</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>6389.5</td>\n",
       "      <td>6320.99</td>\n",
       "      <td>125.35</td>\n",
       "      <td>6057.64</td>\n",
       "      <td>6584.33</td>\n",
       "      <td>5452.43</td>\n",
       "      <td>7189.54</td>\n",
       "      <td>68.51</td>\n",
       "      <td>373.48</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>4152.2</td>\n",
       "      <td>4011.89</td>\n",
       "      <td>143.70</td>\n",
       "      <td>3709.98</td>\n",
       "      <td>4313.80</td>\n",
       "      <td>3130.87</td>\n",
       "      <td>4892.90</td>\n",
       "      <td>140.31</td>\n",
       "      <td>366.81</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>5544.8</td>\n",
       "      <td>4854.96</td>\n",
       "      <td>96.55</td>\n",
       "      <td>4652.12</td>\n",
       "      <td>5057.81</td>\n",
       "      <td>4002.80</td>\n",
       "      <td>5707.13</td>\n",
       "      <td>689.84</td>\n",
       "      <td>381.94</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>6095.1</td>\n",
       "      <td>5946.54</td>\n",
       "      <td>104.55</td>\n",
       "      <td>5726.90</td>\n",
       "      <td>6166.18</td>\n",
       "      <td>5090.22</td>\n",
       "      <td>6802.86</td>\n",
       "      <td>148.56</td>\n",
       "      <td>379.83</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3626.2</td>\n",
       "      <td>3821.83</td>\n",
       "      <td>157.22</td>\n",
       "      <td>3491.53</td>\n",
       "      <td>4152.13</td>\n",
       "      <td>2930.68</td>\n",
       "      <td>4712.97</td>\n",
       "      <td>-195.63</td>\n",
       "      <td>361.23</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3745.4</td>\n",
       "      <td>4074.30</td>\n",
       "      <td>139.41</td>\n",
       "      <td>3781.40</td>\n",
       "      <td>4367.20</td>\n",
       "      <td>3196.33</td>\n",
       "      <td>4952.27</td>\n",
       "      <td>-328.90</td>\n",
       "      <td>368.46</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5121.8</td>\n",
       "      <td>5888.10</td>\n",
       "      <td>101.87</td>\n",
       "      <td>5674.07</td>\n",
       "      <td>6102.13</td>\n",
       "      <td>5033.20</td>\n",
       "      <td>6743.00</td>\n",
       "      <td>-766.30</td>\n",
       "      <td>380.56</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>5674.5</td>\n",
       "      <td>5747.97</td>\n",
       "      <td>96.28</td>\n",
       "      <td>5545.68</td>\n",
       "      <td>5950.25</td>\n",
       "      <td>4895.93</td>\n",
       "      <td>6600.00</td>\n",
       "      <td>-73.47</td>\n",
       "      <td>382.01</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>4256.6</td>\n",
       "      <td>4043.66</td>\n",
       "      <td>141.51</td>\n",
       "      <td>3746.36</td>\n",
       "      <td>4340.96</td>\n",
       "      <td>3164.21</td>\n",
       "      <td>4923.11</td>\n",
       "      <td>212.94</td>\n",
       "      <td>367.66</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>5803.7</td>\n",
       "      <td>6008.95</td>\n",
       "      <td>107.59</td>\n",
       "      <td>5782.90</td>\n",
       "      <td>6234.99</td>\n",
       "      <td>5150.96</td>\n",
       "      <td>6866.93</td>\n",
       "      <td>-205.25</td>\n",
       "      <td>378.98</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Obs   Dep Var Population  Predicted Value  Std Error Mean Predict   \n",
       "0    1.0              4597.5          4264.92                  126.89  \\\n",
       "1    2.0              6611.0          6945.07                  168.77   \n",
       "2    3.0              7349.3          6448.64                  133.55   \n",
       "3    4.0              5525.7          5260.05                   88.18   \n",
       "4    5.0              4675.9          4763.05                  100.13   \n",
       "5    6.0              4418.6          4762.49                  100.15   \n",
       "6    7.0              5845.4          6196.17                  117.80   \n",
       "7    8.0              7313.0          7151.01                  184.43   \n",
       "8    9.0              5035.4          5015.52                   91.69   \n",
       "9   10.0              4322.6          4578.10                  108.79   \n",
       "10  11.0              6389.5          6320.99                  125.35   \n",
       "11  12.0              4152.2          4011.89                  143.70   \n",
       "12  13.0              5544.8          4854.96                   96.55   \n",
       "13  14.0              6095.1          5946.54                  104.55   \n",
       "14  15.0              3626.2          3821.83                  157.22   \n",
       "15  16.0              3745.4          4074.30                  139.41   \n",
       "16  17.0              5121.8          5888.10                  101.87   \n",
       "17  18.0              5674.5          5747.97                   96.28   \n",
       "18  19.0              4256.6          4043.66                  141.51   \n",
       "19  20.0              5803.7          6008.95                  107.59   \n",
       "\n",
       "    Mean ci 95% low  Mean ci 95% upp  Predict ci 95% low  Predict ci 95% upp   \n",
       "0           3998.35          4531.50             3395.38             5134.47  \\\n",
       "1           6590.49          7299.64             6044.64             7845.49   \n",
       "2           6168.06          6729.22             5574.70             7322.58   \n",
       "3           5074.79          5445.31             4411.90             6108.20   \n",
       "4           4552.70          4973.41             3909.07             5617.04   \n",
       "5           4552.08          4972.89             3908.49             5616.48   \n",
       "6           5948.68          6443.66             5332.29             7060.05   \n",
       "7           6763.53          7538.49             6237.13             8064.90   \n",
       "8           4822.89          5208.15             4165.73             5865.32   \n",
       "9           4349.55          4806.65             3719.45             5436.75   \n",
       "10          6057.64          6584.33             5452.43             7189.54   \n",
       "11          3709.98          4313.80             3130.87             4892.90   \n",
       "12          4652.12          5057.81             4002.80             5707.13   \n",
       "13          5726.90          6166.18             5090.22             6802.86   \n",
       "14          3491.53          4152.13             2930.68             4712.97   \n",
       "15          3781.40          4367.20             3196.33             4952.27   \n",
       "16          5674.07          6102.13             5033.20             6743.00   \n",
       "17          5545.68          5950.25             4895.93             6600.00   \n",
       "18          3746.36          4340.96             3164.21             4923.11   \n",
       "19          5782.90          6234.99             5150.96             6866.93   \n",
       "\n",
       "    Residual   Std Error Residual  Student Residual  Cook's D  \n",
       "0      332.58              372.96              0.89      0.05  \n",
       "1     -334.07              355.97             -0.94      0.10  \n",
       "2      900.66              370.63              2.43      0.38  \n",
       "3      265.65              383.96              0.69      0.01  \n",
       "4      -87.15              381.02             -0.23      0.00  \n",
       "5     -343.89              381.01             -0.90      0.03  \n",
       "6     -350.77              375.93             -0.93      0.04  \n",
       "7      161.99              348.12              0.47      0.03  \n",
       "8       19.88              383.14              0.05      0.00  \n",
       "9     -255.50              378.64             -0.67      0.02  \n",
       "10      68.51              373.48              0.18      0.00  \n",
       "11     140.31              366.81              0.38      0.01  \n",
       "12     689.84              381.94              1.81      0.10  \n",
       "13     148.56              379.83              0.39      0.01  \n",
       "14    -195.63              361.23             -0.54      0.03  \n",
       "15    -328.90              368.46             -0.89      0.06  \n",
       "16    -766.30              380.56             -2.01      0.15  \n",
       "17     -73.47              382.01             -0.19      0.00  \n",
       "18     212.94              367.66              0.58      0.02  \n",
       "19    -205.25              378.98             -0.54      0.01  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_level = 0.95 # 设置置信水平\n",
    "st, _, _ = summary_table(model, alpha=1-conf_level) # 计算置信区间\n",
    "columns = [x + ' ' + y for x, y in zip(st.data[0], st.data[1])] # 设置列名\n",
    "df_res = pd.DataFrame() # 创建空的数据框\n",
    "for i in range(len(st.data) - 2):\n",
    "    df_res = pd.concat([df_res, pd.DataFrame(st.data[i+2], index=columns).T], ignore_index=True) # 将置信区间添加到数据框中\n",
    "df_res.reset_index(drop=True, inplace=True) # 重置索引\n",
    "round(df_res, 2) # 显示结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_res['广告支出'] = example9_1['广告支出'] # 添加广告支出变量\n",
    "df_plot = df_res.sort_values(by='广告支出') # 按照广告支出变量排序\n",
    "df_plot.reset_index(drop=True, inplace=True) # 重置索引\n",
    "\n",
    "plt.figure(figsize=(8, 5)) # 设置画布大小\n",
    "plt.scatter(df_plot['广告支出'], df_plot['Dep Var Population']) # 绘制散点图\n",
    "p1, = plt.plot(df_plot['广告支出'], df_plot['Predicted Value']) # 绘制回归拟合线\n",
    "p2, = plt.plot(df_plot['广告支出'], df_plot['Mean ci 95% low'], 'r:') # 绘制置信区间下限\n",
    "p3, = plt.plot(df_plot['广告支出'], df_plot['Mean ci 95% upp'], 'r:') # 绘制置信区间上限\n",
    "p4, = plt.plot(df_plot['广告支出'], df_plot['Predict ci 95% low'], 'g--') # 绘制预测区间下限\n",
    "p5, = plt.plot(df_plot['广告支出'], df_plot['Predict ci 95% upp'], 'g--') # 绘制预测区间上限\n",
    "plt.xlabel('广告支出') # 设置x轴标签\n",
    "plt.ylabel('销售收入') # 设置y轴标签\n",
    "plt.legend([p1, p2, p4], ['回归线', '置信区间', '预测区间']) # 设置图例\n",
    "plt.show() # 展示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.4 回归模型的诊断"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.4.1 残差与残差图"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "残差：$e_i = y_i - \\hat{y}_i$\n",
    "\n",
    "标准化残差：$z_{e_i} = \\frac{e_i}{s_e} = \\frac{y_i - \\hat{y}_i}{s_e}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企业编号</th>\n",
       "      <th>销售收入</th>\n",
       "      <th>点预测值</th>\n",
       "      <th>残差</th>\n",
       "      <th>标准化残差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4597.5</td>\n",
       "      <td>4264.9246</td>\n",
       "      <td>332.5754</td>\n",
       "      <td>0.8442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6611.0</td>\n",
       "      <td>6945.0665</td>\n",
       "      <td>-334.0665</td>\n",
       "      <td>-0.8480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7349.3</td>\n",
       "      <td>6448.6388</td>\n",
       "      <td>900.6612</td>\n",
       "      <td>2.2862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5525.7</td>\n",
       "      <td>5260.0493</td>\n",
       "      <td>265.6507</td>\n",
       "      <td>0.6743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4675.9</td>\n",
       "      <td>4763.0543</td>\n",
       "      <td>-87.1543</td>\n",
       "      <td>-0.2212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4418.6</td>\n",
       "      <td>4762.4870</td>\n",
       "      <td>-343.8870</td>\n",
       "      <td>-0.8729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>5845.4</td>\n",
       "      <td>6196.1699</td>\n",
       "      <td>-350.7699</td>\n",
       "      <td>-0.8904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>7313.0</td>\n",
       "      <td>7151.0130</td>\n",
       "      <td>161.9870</td>\n",
       "      <td>0.4112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5035.4</td>\n",
       "      <td>5015.5232</td>\n",
       "      <td>19.8768</td>\n",
       "      <td>0.0505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4322.6</td>\n",
       "      <td>4578.0996</td>\n",
       "      <td>-255.4996</td>\n",
       "      <td>-0.6485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>6389.5</td>\n",
       "      <td>6320.9860</td>\n",
       "      <td>68.5140</td>\n",
       "      <td>0.1739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>4152.2</td>\n",
       "      <td>4011.8884</td>\n",
       "      <td>140.3116</td>\n",
       "      <td>0.3562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>5544.8</td>\n",
       "      <td>4854.9643</td>\n",
       "      <td>689.8357</td>\n",
       "      <td>1.7510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>6095.1</td>\n",
       "      <td>5946.5378</td>\n",
       "      <td>148.5622</td>\n",
       "      <td>0.3771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>3626.2</td>\n",
       "      <td>3821.8275</td>\n",
       "      <td>-195.6275</td>\n",
       "      <td>-0.4966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>3745.4</td>\n",
       "      <td>4074.2964</td>\n",
       "      <td>-328.8964</td>\n",
       "      <td>-0.8349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>5121.8</td>\n",
       "      <td>5888.1011</td>\n",
       "      <td>-766.3011</td>\n",
       "      <td>-1.9451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>5674.5</td>\n",
       "      <td>5747.9667</td>\n",
       "      <td>-73.4667</td>\n",
       "      <td>-0.1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>4256.6</td>\n",
       "      <td>4043.6598</td>\n",
       "      <td>212.9402</td>\n",
       "      <td>0.5405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>5803.7</td>\n",
       "      <td>6008.9458</td>\n",
       "      <td>-205.2458</td>\n",
       "      <td>-0.5210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    企业编号    销售收入       点预测值        残差   标准化残差\n",
       "0      1  4597.5  4264.9246  332.5754  0.8442\n",
       "1      2  6611.0  6945.0665 -334.0665 -0.8480\n",
       "2      3  7349.3  6448.6388  900.6612  2.2862\n",
       "3      4  5525.7  5260.0493  265.6507  0.6743\n",
       "4      5  4675.9  4763.0543  -87.1543 -0.2212\n",
       "5      6  4418.6  4762.4870 -343.8870 -0.8729\n",
       "6      7  5845.4  6196.1699 -350.7699 -0.8904\n",
       "7      8  7313.0  7151.0130  161.9870  0.4112\n",
       "8      9  5035.4  5015.5232   19.8768  0.0505\n",
       "9     10  4322.6  4578.0996 -255.4996 -0.6485\n",
       "10    11  6389.5  6320.9860   68.5140  0.1739\n",
       "11    12  4152.2  4011.8884  140.3116  0.3562\n",
       "12    13  5544.8  4854.9643  689.8357  1.7510\n",
       "13    14  6095.1  5946.5378  148.5622  0.3771\n",
       "14    15  3626.2  3821.8275 -195.6275 -0.4966\n",
       "15    16  3745.4  4074.2964 -328.8964 -0.8349\n",
       "16    17  5121.8  5888.1011 -766.3011 -1.9451\n",
       "17    18  5674.5  5747.9667  -73.4667 -0.1865\n",
       "18    19  4256.6  4043.6598  212.9402  0.5405\n",
       "19    20  5803.7  6008.9458 -205.2458 -0.5210"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    '企业编号': example9_1['企业编号'],\n",
    "    '销售收入': example9_1['销售收入'],\n",
    "    '点预测值': model.fittedvalues,\n",
    "    '残差': model.resid,\n",
    "    '标准化残差': model.resid_pearson\n",
    "}) # 创建数据框\n",
    "round(df, 4) # 显示数据框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制不同形态的残差图\n",
    "np.random.seed(1234) # 设置随机种子\n",
    "plt.subplots(1, 3, figsize=(15, 4)) # 设置画布大小\n",
    "\n",
    "plt.subplot(131) # 创建第一个子图\n",
    "x1 = np.random.uniform(0, 20, 50) # 创建自变量\n",
    "e1 = np.random.normal(0, 3, 50) # 创建误差项\n",
    "plt.plot(x1, e1, '+') # 绘制散点图\n",
    "plt.axhline(0, linestyle='--') # 绘制e=0的直线\n",
    "plt.axhline(-6, linestyle=':') # 绘制e=-6的直线\n",
    "plt.axhline(6, linestyle=':') # 绘制e=6的直线\n",
    "plt.xlabel('x1') # 设置x轴标签\n",
    "plt.ylabel('e1') # 设置y轴标签\n",
    "plt.ylim(-8, 8)\n",
    "plt.title('(a) 满意的模式')\n",
    "\n",
    "plt.subplot(132) # 创建第二个子图\n",
    "x2 = np.random.uniform(0, 20, 50) # 创建自变量\n",
    "e2 = np.random.normal(0, 3, 50) # 创建误差项\n",
    "e2 = x2 ** 0.4 * e2 # 修改误差项, 使其与自变量相关\n",
    "plt.plot(x2, e2, '+') # 绘制散点图\n",
    "plt.axhline(0, linestyle='--') # 绘制e=0的直线\n",
    "plt.plot([0, 20], [5, 20], ':') # 绘制上限\n",
    "plt.plot([0, 20], [-5, -20], ':') # 绘制下限\n",
    "plt.xlabel('x2') # 设置x轴标签\n",
    "plt.ylabel('e2') # 设置y轴标签\n",
    "plt.ylim(-20, 20)\n",
    "plt.title('(b) 非常数方差')\n",
    "\n",
    "plt.subplot(133) # 创建第三个子图\n",
    "x3 = np.random.uniform(0, 20, 50) # 创建自变量\n",
    "y = lambda x: - 0.1 * x ** 2 + 2.5 * x - 10 # 创建函数\n",
    "e3 = y(x3) + np.random.normal(0, 5, 50) # 创建误差项\n",
    "plt.plot(x3, e3, '+') # 绘制散点图\n",
    "plt.plot(np.linspace(0, 20, 50), y(np.linspace(0, 20, 50)), '-') # 绘制函数图像\n",
    "plt.axhline(0, linestyle='--') # 绘制e=0的直线\n",
    "plt.xlabel('x3') # 设置x轴标签\n",
    "plt.ylabel('e3') # 设置y轴标签\n",
    "plt.ylim(-15, 15)\n",
    "plt.title('(c) 模型形式不合适')\n",
    "\n",
    "plt.tight_layout() # 设置子图的间距\n",
    "plt.show() # 展示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.4.2 检验模型假定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 残差的正态性与方差齐性\n",
    "plt.subplots(1, 2, figsize=(10, 4)) # 设置画布大小\n",
    "\n",
    "plt.subplot(121) # 创建第一个子图\n",
    "plt.scatter(model.fittedvalues, model.resid) # 绘制散点图\n",
    "plt.axhline(0, linestyle='--') # 绘制e=0的直线\n",
    "plt.xlabel('拟合值') # 设置x轴标签\n",
    "plt.ylabel('残差') # 设置y轴标签\n",
    "plt.title('(a) 残差值与拟合值图', fontsize=15) # 设置子图标题\n",
    "\n",
    "ax2 = plt.subplot(122) # 创建第二个子图\n",
    "pplot = sm.ProbPlot(model.resid, fit=True) # 创建概率图对象\n",
    "pplot.qqplot(line='r', ax=ax2, xlabel='期望正态值', ylabel='标准化的观测值') # 绘制Q-Q图\n",
    "ax2.set_title('(b) 正态Q-Q图', fontsize=15) # 设置子图标题\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durbin-Watson检验的值为1.68\n"
     ]
    }
   ],
   "source": [
    "# 残差是否独立（无自相关性）\n",
    "# Durbin-Watson检验\n",
    "dw = durbin_watson(model.resid) # 计算DW检验的值\n",
    "print(f'Durbin-Watson检验的值为{dw:.2f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 习题"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "      <th>y4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8.04</td>\n",
       "      <td>9.14</td>\n",
       "      <td>7.46</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6.95</td>\n",
       "      <td>8.14</td>\n",
       "      <td>6.77</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>7.58</td>\n",
       "      <td>8.74</td>\n",
       "      <td>12.74</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>8.81</td>\n",
       "      <td>8.77</td>\n",
       "      <td>7.11</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>8.33</td>\n",
       "      <td>9.26</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.84</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4.26</td>\n",
       "      <td>3.10</td>\n",
       "      <td>5.39</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.13</td>\n",
       "      <td>8.15</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7.26</td>\n",
       "      <td>6.42</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5.68</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x1  x2  x3  x4     y1    y2     y3     y4\n",
       "0   10  10  10   8   8.04  9.14   7.46   6.58\n",
       "1    8   8   8   8   6.95  8.14   6.77   5.76\n",
       "2   13  13  13   8   7.58  8.74  12.74   7.71\n",
       "3    9   9   9   8   8.81  8.77   7.11   8.84\n",
       "4   11  11  11   8   8.33  9.26   7.81   8.47\n",
       "5   14  14  14   8   9.96  8.10   8.84   7.04\n",
       "6    6   6   6   8   7.24  6.13   6.08   5.25\n",
       "7    4   4   4  19   4.26  3.10   5.39  12.50\n",
       "8   12  12  12   8  10.84  9.13   8.15   5.56\n",
       "9    7   7   7   8   4.82  7.26   6.42   7.91\n",
       "10   5   5   5   8   5.68  4.74   5.73   6.89"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap09/exercise9_1.csv', encoding='gbk') # 读取数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "plt.subplots(2, 2, figsize=(8, 6)) # 设置画布大小\n",
    "\n",
    "plt.subplot(221) # 创建第一个子图\n",
    "sns.regplot(x='x1', y='y1', data=df, marker='+') # 绘制散点图和回归线\n",
    "plt.xlabel('x1') # 设置x轴标签\n",
    "plt.ylabel('y1') # 设置y轴标签\n",
    "plt.title('(a) x1与y1的散点图及回归线') # 设置子图标题\n",
    "\n",
    "plt.subplot(222) # 创建第二个子图\n",
    "sns.regplot(x='x2', y='y2', data=df, marker='+') # 绘制散点图和回归线\n",
    "plt.xlabel('x2') # 设置x轴标签\n",
    "plt.ylabel('y2') # 设置y轴标签\n",
    "plt.title('(b) x2与y2的散点图及回归线') # 设置子图标题\n",
    "\n",
    "plt.subplot(223) # 创建第三个子图\n",
    "sns.regplot(x='x3', y='y3', data=df, marker='+') # 绘制散点图和回归线\n",
    "plt.xlabel('x3') # 设置x轴标签\n",
    "plt.ylabel('y3') # 设置y轴标签\n",
    "plt.title('(c) x3与y3的散点图及回归线') # 设置子图标题\n",
    "\n",
    "plt.subplot(224) # 创建第四个子图\n",
    "sns.regplot(x='x4', y='y4', data=df, marker='+') # 绘制散点图和回归线\n",
    "plt.xlabel('x4') # 设置x轴标签\n",
    "plt.ylabel('y4') # 设置y轴标签\n",
    "plt.title('(d) x4与y4的散点图及回归线') # 设置子图标题\n",
    "\n",
    "plt.tight_layout() # 设置子图的间距\n",
    "plt.show() # 展示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的散点图和回归直线的拟合来看：\n",
    "- 图 (a) 的的拟合效果最好，因为散点图的点比较集中在回归直线附近，且分布随机。\n",
    "- 图 (b) 中散点的分布不适用于线性回归，呈现出非线性关系(如二次函数关系)。\n",
    "- 图 (c) 的拟合效果一般，主要是因为存在一个离群点，对回归直线的拟合造成了影响。\n",
    "- 图 (d) 中散点分布比较极端，主要集中在x4=8上，但是由于有一个点在x4=19上，导致了拟合结果不伦不类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zmy/mambaforge/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y1</td>        <th>  R-squared:         </th> <td>   0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   17.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th>  <td>0.00217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:15:23</td>     <th>  Log-Likelihood:    </th> <td> -16.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    11</td>      <th>  AIC:               </th> <td>   37.68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   38.48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    3.0001</td> <td>    1.125</td> <td>    2.667</td> <td> 0.026</td> <td>    0.456</td> <td>    5.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>        <td>    0.5001</td> <td>    0.118</td> <td>    4.241</td> <td> 0.002</td> <td>    0.233</td> <td>    0.767</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.082</td> <th>  Durbin-Watson:     </th> <td>   3.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.960</td> <th>  Jarque-Bera (JB):  </th> <td>   0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.122</td> <th>  Prob(JB):          </th> <td>   0.865</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.244</td> <th>  Cond. No.          </th> <td>    29.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     y1   R-squared:                       0.667\n",
       "Model:                            OLS   Adj. R-squared:                  0.629\n",
       "Method:                 Least Squares   F-statistic:                     17.99\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):            0.00217\n",
       "Time:                        21:15:23   Log-Likelihood:                -16.841\n",
       "No. Observations:                  11   AIC:                             37.68\n",
       "Df Residuals:                       9   BIC:                             38.48\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      3.0001      1.125      2.667      0.026       0.456       5.544\n",
       "x1             0.5001      0.118      4.241      0.002       0.233       0.767\n",
       "==============================================================================\n",
       "Omnibus:                        0.082   Durbin-Watson:                   3.212\n",
       "Prob(Omnibus):                  0.960   Jarque-Bera (JB):                0.289\n",
       "Skew:                          -0.122   Prob(JB):                        0.865\n",
       "Kurtosis:                       2.244   Cond. No.                         29.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = ols('y1 ~ x1', data=df).fit() # 拟合普通最小二乘回归模型\n",
    "model1.summary() # 显示回归结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zmy/mambaforge/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y2</td>        <th>  R-squared:         </th> <td>   0.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   17.97</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th>  <td>0.00218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:15:39</td>     <th>  Log-Likelihood:    </th> <td> -16.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    11</td>      <th>  AIC:               </th> <td>   37.69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   38.49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    3.0009</td> <td>    1.125</td> <td>    2.667</td> <td> 0.026</td> <td>    0.455</td> <td>    5.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>        <td>    0.5000</td> <td>    0.118</td> <td>    4.239</td> <td> 0.002</td> <td>    0.233</td> <td>    0.767</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.594</td> <th>  Durbin-Watson:     </th> <td>   2.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.451</td> <th>  Jarque-Bera (JB):  </th> <td>   1.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.567</td> <th>  Prob(JB):          </th> <td>   0.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.936</td> <th>  Cond. No.          </th> <td>    29.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     y2   R-squared:                       0.666\n",
       "Model:                            OLS   Adj. R-squared:                  0.629\n",
       "Method:                 Least Squares   F-statistic:                     17.97\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):            0.00218\n",
       "Time:                        21:15:39   Log-Likelihood:                -16.846\n",
       "No. Observations:                  11   AIC:                             37.69\n",
       "Df Residuals:                       9   BIC:                             38.49\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      3.0009      1.125      2.667      0.026       0.455       5.547\n",
       "x2             0.5000      0.118      4.239      0.002       0.233       0.767\n",
       "==============================================================================\n",
       "Omnibus:                        1.594   Durbin-Watson:                   2.188\n",
       "Prob(Omnibus):                  0.451   Jarque-Bera (JB):                1.108\n",
       "Skew:                          -0.567   Prob(JB):                        0.575\n",
       "Kurtosis:                       1.936   Cond. No.                         29.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = ols('y2 ~ x2', data=df).fit() # 拟合普通最小二乘回归模型\n",
    "model2.summary() # 显示回归结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zmy/mambaforge/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y3</td>        <th>  R-squared:         </th> <td>   0.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   17.97</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th>  <td>0.00218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:15:56</td>     <th>  Log-Likelihood:    </th> <td> -16.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    11</td>      <th>  AIC:               </th> <td>   37.68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   38.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    3.0025</td> <td>    1.124</td> <td>    2.670</td> <td> 0.026</td> <td>    0.459</td> <td>    5.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>        <td>    0.4997</td> <td>    0.118</td> <td>    4.239</td> <td> 0.002</td> <td>    0.233</td> <td>    0.766</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>19.540</td> <th>  Durbin-Watson:     </th> <td>   2.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  13.478</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.041</td> <th>  Prob(JB):          </th> <td> 0.00118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.571</td> <th>  Cond. No.          </th> <td>    29.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     y3   R-squared:                       0.666\n",
       "Model:                            OLS   Adj. R-squared:                  0.629\n",
       "Method:                 Least Squares   F-statistic:                     17.97\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):            0.00218\n",
       "Time:                        21:15:56   Log-Likelihood:                -16.838\n",
       "No. Observations:                  11   AIC:                             37.68\n",
       "Df Residuals:                       9   BIC:                             38.47\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      3.0025      1.124      2.670      0.026       0.459       5.546\n",
       "x3             0.4997      0.118      4.239      0.002       0.233       0.766\n",
       "==============================================================================\n",
       "Omnibus:                       19.540   Durbin-Watson:                   2.144\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               13.478\n",
       "Skew:                           2.041   Prob(JB):                      0.00118\n",
       "Kurtosis:                       6.571   Cond. No.                         29.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = ols('y3 ~ x3', data=df).fit() # 拟合普通最小二乘回归模型\n",
    "model3.summary() # 显示回归结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zmy/mambaforge/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y4</td>        <th>  R-squared:         </th> <td>   0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   18.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th>  <td>0.00216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:16:06</td>     <th>  Log-Likelihood:    </th> <td> -16.833</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    11</td>      <th>  AIC:               </th> <td>   37.67</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   38.46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    3.0017</td> <td>    1.124</td> <td>    2.671</td> <td> 0.026</td> <td>    0.459</td> <td>    5.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>        <td>    0.4999</td> <td>    0.118</td> <td>    4.243</td> <td> 0.002</td> <td>    0.233</td> <td>    0.766</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.555</td> <th>  Durbin-Watson:     </th> <td>   1.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.758</td> <th>  Jarque-Bera (JB):  </th> <td>   0.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.010</td> <th>  Prob(JB):          </th> <td>   0.769</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.931</td> <th>  Cond. No.          </th> <td>    29.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     y4   R-squared:                       0.667\n",
       "Model:                            OLS   Adj. R-squared:                  0.630\n",
       "Method:                 Least Squares   F-statistic:                     18.00\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):            0.00216\n",
       "Time:                        21:16:06   Log-Likelihood:                -16.833\n",
       "No. Observations:                  11   AIC:                             37.67\n",
       "Df Residuals:                       9   BIC:                             38.46\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      3.0017      1.124      2.671      0.026       0.459       5.544\n",
       "x4             0.4999      0.118      4.243      0.002       0.233       0.766\n",
       "==============================================================================\n",
       "Omnibus:                        0.555   Durbin-Watson:                   1.662\n",
       "Prob(Omnibus):                  0.758   Jarque-Bera (JB):                0.524\n",
       "Skew:                           0.010   Prob(JB):                        0.769\n",
       "Kurtosis:                       1.931   Cond. No.                         29.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4 = ols('y4 ~ x4', data=df).fit() # 拟合普通最小二乘回归模型\n",
    "model4.summary() # 显示回归结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对数据分别进行回归分析，可以发现：\n",
    "- 四个模型都通过了显著性检验，即拟合效果都是显著的。\n",
    "- 四个模型的决定系数均大于0.6，拟合优度较高。\n",
    "\n",
    "但是，结合前文对数据可视化的结果表明：\n",
    "- 尽管回归结果看起来一切合理，但是却有可能是陷阱。\n",
    "- 在回归前对数据进行可视化分析是非常重要的，数无形时少直觉。\n",
    "- 在回归后也要对回归模型进行诊断，以确保回归结果的可靠性。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>航空公司编号</th>\n",
       "      <th>航班准点率</th>\n",
       "      <th>投诉次数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>81.8</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>76.6</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>76.6</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>75.7</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>73.8</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>72.2</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>71.2</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>70.8</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>91.4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>68.5</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   航空公司编号  航班准点率  投诉次数\n",
       "0       1   81.8    21\n",
       "1       2   76.6    58\n",
       "2       3   76.6    85\n",
       "3       4   75.7    68\n",
       "4       5   73.8    74\n",
       "5       6   72.2    93\n",
       "6       7   71.2    72\n",
       "7       8   70.8   122\n",
       "8       9   91.4    18\n",
       "9      10   68.5   125"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap09/exercise9_2.csv', encoding='gbk') # 读取数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zmy/mambaforge/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>投诉次数</td>       <th>  R-squared:         </th> <td>   0.755</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   24.59</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th>  <td>0.00111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:39:46</td>     <th>  Log-Likelihood:    </th> <td> -42.459</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    10</td>      <th>  AIC:               </th> <td>   88.92</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     8</td>      <th>  BIC:               </th> <td>   89.52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  430.1892</td> <td>   72.155</td> <td>    5.962</td> <td> 0.000</td> <td>  263.800</td> <td>  596.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>航班准点率</th>     <td>   -4.7006</td> <td>    0.948</td> <td>   -4.959</td> <td> 0.001</td> <td>   -6.886</td> <td>   -2.515</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.048</td> <th>  Durbin-Watson:     </th> <td>   1.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.359</td> <th>  Jarque-Bera (JB):  </th> <td>   0.833</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.053</td> <th>  Prob(JB):          </th> <td>   0.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.590</td> <th>  Cond. No.          </th> <td>    920.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   投诉次数   R-squared:                       0.755\n",
       "Model:                            OLS   Adj. R-squared:                  0.724\n",
       "Method:                 Least Squares   F-statistic:                     24.59\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):            0.00111\n",
       "Time:                        21:39:46   Log-Likelihood:                -42.459\n",
       "No. Observations:                  10   AIC:                             88.92\n",
       "Df Residuals:                       8   BIC:                             89.52\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    430.1892     72.155      5.962      0.000     263.800     596.579\n",
       "航班准点率         -4.7006      0.948     -4.959      0.001      -6.886      -2.515\n",
       "==============================================================================\n",
       "Omnibus:                        2.048   Durbin-Watson:                   1.579\n",
       "Prob(Omnibus):                  0.359   Jarque-Bera (JB):                0.833\n",
       "Skew:                          -0.053   Prob(JB):                        0.659\n",
       "Kurtosis:                       1.590   Cond. No.                         920.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 用航班准点率作自变量，顾客投诉次数作因变量，求出估计的回归方程，并解释回归系数的意义。\n",
    "model = ols('投诉次数 ~ 航班准点率', data=df).fit() # 拟合普通最小二乘回归模型\n",
    "model.summary() # 显示回归结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "估计的回归方程为：$\\hat{y} = 430.1892 - 4.7006 \\times 航班准点率$\n",
    "\n",
    "回归系数的意义：\n",
    "- 截距项：当航班准点率为0时，顾客投诉数为430.1892，在本例中没有实际意义。\n",
    "- 航班准点率的回归系数：当航班准点率每增加1%，顾客投诉数减少4.7006个。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "航班准点率回归系数的t检验\n",
      "t值为-4.96, p值为0.001108\n"
     ]
    }
   ],
   "source": [
    "# (2) 检验回归系数的显著性(α=0.05)。\n",
    "print('航班准点率回归系数的t检验')\n",
    "print(f't值为{model.tvalues[1]:.2f}, p值为{model.pvalues[1]:.4g}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05, 所以航班准点率回归系数显著不为0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当航班准点率为80%时, 预计顾客的投诉次数为54.14\n"
     ]
    }
   ],
   "source": [
    "# (3) 如果航班准点率为80%，估计顾客的投诉次数。\n",
    "y_hat = model.params[0] + model.params[1] * 80 # 计算预测值\n",
    "print(f'当航班准点率为80%时, 预计顾客的投诉次数为{y_hat:.2f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>超市</th>\n",
       "      <th>广告费支出/万元</th>\n",
       "      <th>销售额/万元</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>10</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>20</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  超市  广告费支出/万元  销售额/万元\n",
       "0  A         1      19\n",
       "1  B         2      32\n",
       "2  C         4      44\n",
       "3  D         6      40\n",
       "4  E        10      52\n",
       "5  F        14      53\n",
       "6  G        20      54"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap09/exercise9_3.csv', encoding='gbk') # 读取数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zmy/mambaforge/lib/python3.10/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 7 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>销售额</td>       <th>  R-squared:         </th> <td>   0.690</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   11.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th>  <td>0.0206</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:52:31</td>     <th>  Log-Likelihood:    </th> <td> -23.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>     7</td>      <th>  AIC:               </th> <td>   50.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     5</td>      <th>  BIC:               </th> <td>   50.30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   29.3991</td> <td>    4.807</td> <td>    6.116</td> <td> 0.002</td> <td>   17.042</td> <td>   41.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>广告费支出</th>     <td>    1.5475</td> <td>    0.463</td> <td>    3.339</td> <td> 0.021</td> <td>    0.356</td> <td>    2.739</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>   nan</td> <th>  Durbin-Watson:     </th> <td>   1.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td>   nan</td> <th>  Jarque-Bera (JB):  </th> <td>   0.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.481</td> <th>  Prob(JB):          </th> <td>   0.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.152</td> <th>  Cond. No.          </th> <td>    16.8</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    销售额   R-squared:                       0.690\n",
       "Model:                            OLS   Adj. R-squared:                  0.628\n",
       "Method:                 Least Squares   F-statistic:                     11.15\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):             0.0206\n",
       "Time:                        21:52:31   Log-Likelihood:                -23.203\n",
       "No. Observations:                   7   AIC:                             50.41\n",
       "Df Residuals:                       5   BIC:                             50.30\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     29.3991      4.807      6.116      0.002      17.042      41.757\n",
       "广告费支出          1.5475      0.463      3.339      0.021       0.356       2.739\n",
       "==============================================================================\n",
       "Omnibus:                          nan   Durbin-Watson:                   1.257\n",
       "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.480\n",
       "Skew:                          -0.481   Prob(JB):                        0.787\n",
       "Kurtosis:                       2.152   Cond. No.                         16.8\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['超市', '广告费支出', '销售额'] # 重命名列名\n",
    "model = ols('销售额 ~ 广告费支出', data=df).fit() # 拟合普通最小二乘回归模型\n",
    "model.summary() # 显示回归结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "估计的回归方程为: $\\hat {y}=29.3991+1.5475 \\times$ 广告费支出\n",
    "\n",
    "回归系数的意义：\n",
    "- 截距项：当广告费支出为0时，销售额为29.3991万元。\n",
    "- 广告费支出的回归系数：当广告费支出每增加1万元，销售额增加1.5475万元。\n",
    "\n",
    "回归模型总体显著(F检验)，各回归系数均显著(t检验)，拟合优度较高(决定系数大于0.6)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 残差的正态性与方差齐性\n",
    "plt.subplots(1, 2, figsize=(10, 4)) # 设置画布大小\n",
    "\n",
    "plt.subplot(121) # 创建第一个子图\n",
    "plt.scatter(model.fittedvalues, model.resid) # 绘制散点图\n",
    "plt.axhline(0, linestyle='--') # 绘制e=0的直线\n",
    "plt.xlabel('拟合值') # 设置x轴标签\n",
    "plt.ylabel('残差') # 设置y轴标签\n",
    "plt.title('(a) 残差值与拟合值图', fontsize=15) # 设置子图标题\n",
    "\n",
    "ax2 = plt.subplot(122) # 创建第二个子图\n",
    "pplot = sm.ProbPlot(model.resid, fit=True) # 创建概率图对象\n",
    "pplot.qqplot(line='r', ax=ax2, xlabel='期望正态值', ylabel='标准化的观测值') # 绘制Q-Q图\n",
    "ax2.set_title('(b) 正态Q-Q图', fontsize=15) # 设置子图标题\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型拟合残差呈现以0轴为中心的随机分布，没有明显的异方差现象，基本符合正态性和方差齐性的假定。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
